Knowledge of a rotating machine and its associated control system dynamic parameters is essential for power system transient stability simulation studies. A typical transmission power system model includes a number of frequency dependent components such as impedances, sources and loads. However, the most complex dynamic models with the largest number of variables (for example, greater than 100) to identify and tune belong to synchronous machines. What makes these parameters complex is that they are sometimes independent of each other but they still impact the overall response of the machine to an event (such as load shedding, power system fault or large load addition into the system). Examples of such parameters include machine impedances and time constants, inertial and damping coefficients, prime mover and governor parameters, excitation system and AVR parameters, power system stabilizer (PSS) parameters, and load characteristics. Accuracy in hundreds of these parameters directly affects the credibility of the simulation results. In order to verify and validate (V&V) simulation studies, it is common to compare simulation results against field test measurements such as a generator load acceptance and rejection test. However, expertise is required to understand the effect of each parameter, as the simulation model is manually tuned to provide a similar response as that observed in field tests or recordings. The task of adjusting each variable via the trial-and-error approach is a tedious and time-consuming one. A dynamic tuned model is invaluable to a transmission planner and their regulatory agencies in order to understand the dynamic response of a system as a function of time to potential disturbances in the system.
Dynamic parameter tuning (DPT) tunes parameters of dynamic models. Given transfer function model structures (e.g., exciters, governors, power system stabilizers, generators, wind turbines, electrical machines, FACTS devices, controllers), typical values of model parameters, power system network (if available), and field recorded data using smart sensor devices like PMUs, DPT tunes parameters of dynamic model (e.g., gains, transfer functions, integrators, derivative, time constants, limiters, saturation constants, dead zones, delay) where deviation between the recorded data and the calculated output of the model using the tuned parameters is minimum. In other words, DPT can be used to estimate the values of the parameters that make the controllers respond as similar as possible to a field measured response (i.e. measurements from a staged test or field recorded disturbance). The tuning response can be accomplished by using an iterative approach that automatically adjusts the tunable settings or parameters in the model to make the controller response match that of field recorded data. This process may also be known as automatic model validation parameter tuning.
A phasor measurement unit (PMU) or synchrophasor is a device that measures the electrical waves on an electricity grid, using a common time source for synchronization. A phasor is a complex number that represents both the magnitude and phase angle of the sine waves found in electricity. Time synchronization allows synchronized real-time measurements of multiple remote measurement points on the grid. In power engineering, these are also commonly referred to as synchrophasors and are considered one of the most important measuring devices in the future of power systems. A PMU can be a dedicated device, or the PMU function can be incorporated into a protective relay or other device.
DPT is a complex constraint optimization problem in huge complex multi-dimensional search space because the above mentioned dynamic systems are highly non-linear with limiters (saturations) and are highly sensitive to parameters; they have multiple inputs/outputs (multi-objective) and multiple solutions exist.
Particle swarm optimization (PSO) is a promising optimization method for engineering applications today. It is a swarm based iterative optimization method. Each potential solution, called a particle, flies in a multi-dimensional search space with a velocity, and the velocity is dynamically adjusted according to the flying experience of its own and other particles.
The least squares method is typically used for parameter identification (PI). Few products are available in the market for PI using mainly least square method and not a single product is available for parameter tuning (PT) where any intelligent optimization method is used.
It is very difficult and time consuming to tune the dynamic model parameters from time domain input and output values (curves or data points) because of complex relationships and high sensitivity. It is a complex constraint optimization problem in complex search space with thousands of data points including limits. Practical control systems have many complex control blocks with saturation limits (gains, transfer functions, integrators, derivative, time constants, limiters, saturation constants, dead zones, delay, etc.), and thus the traditional least square method is not suitable mainly for DPT where balance between local and global search is very important for fine tuning.